CN112255113A - High-temperature elastic constant measuring method for thin strip - Google Patents

High-temperature elastic constant measuring method for thin strip Download PDF

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CN112255113A
CN112255113A CN202011072682.9A CN202011072682A CN112255113A CN 112255113 A CN112255113 A CN 112255113A CN 202011072682 A CN202011072682 A CN 202011072682A CN 112255113 A CN112255113 A CN 112255113A
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temperature
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CN112255113B (en
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张悦
郭广平
许巍
刘帅
贾崇林
于慧臣
何玉怀
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AECC Beijing Institute of Aeronautical Materials
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N3/00—Investigating strength properties of solid materials by application of mechanical stress
    • G01N3/08—Investigating strength properties of solid materials by application of mechanical stress by applying steady tensile or compressive forces
    • G01N3/18—Performing tests at high or low temperatures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2203/00—Investigating strength properties of solid materials by application of mechanical stress
    • G01N2203/02—Details not specific for a particular testing method
    • G01N2203/022—Environment of the test
    • G01N2203/0222—Temperature
    • G01N2203/0226—High temperature; Heating means
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10004—Still image; Photographic image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]
    • G—PHYSICS
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    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30108—Industrial image inspection
    • G06T2207/30136—Metal
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2210/00—Indexing scheme for image generation or computer graphics
    • G06T2210/44—Morphing

Abstract

The invention discloses a high-temperature elastic constant measuring method for a thin strip, which comprises the following steps: (1) preparing a flat-plate high-temperature tensile sample, and manufacturing high-temperature speckles on the surface of the sample; (2) respectively collecting a reference image and a loading image of a flat-plate type high-temperature tensile sample; (3) identifying a high-reliability square subregion in a reference image; (4) comparing the reference image with the loaded image by using a digital image correlation method, and calculating the horizontal and vertical displacement of the high-reliability subarea after loading; (5) and obtaining the elastic modulus and the Poisson ratio of the sample material by weighted least square fitting based on the calculated transverse and longitudinal displacements of the high-reliability subarea after loading and the corresponding loads. The method of the invention can not only overcome the problem that the correlation coefficient of the image part area is reduced due to the decorrelation effect, but also effectively eliminate the random error caused by high-temperature heat flow disturbance, and can finish the high-efficiency and accurate measurement of the high-temperature elastic constant.

Description

High-temperature elastic constant measuring method for thin strip
Technical Field
The invention belongs to the technical field of measurement, and particularly relates to a high-temperature elastic constant measurement method for a thin strip.
Background
The strip and the foil are one of the metallurgical products of the deformed high-temperature alloy, and have the advantages of good obdurability, high use temperature, corrosion resistance and the like. In the modern aerospace industry, parts such as turbine cooling air ducts, turbine casing sealing sheets and the like of an aircraft engine made of strip foils need to bear the high temperature of over 800 ℃ in a working state, and high-temperature materials applied to the structures can be subjected to the coupling effect of thermal deformation and mechanical deformation, so that the service life is seriously reduced, and therefore, the guarantee of the service reliability is very important. The elastic constants such as the elastic modulus and the poisson ratio reflect the rigidity of the material, and are very important performance for design. The measurement methods of the elastic constant are mainly classified into a dynamic method and a static method. The dynamic method utilizes methods such as resonance, ultrasound and the like to complete the measurement of the elastic constant of the material, but the method is difficult to obtain the dynamic response of the ultrathin material. The static method utilizes the transverse and longitudinal strain borne by the material in a quasi-static mechanical test to measure the elastic constant, wherein the deformation measurement is an important link in the static method test. Under the high temperature environment of more than 1000 ℃, the application of the traditional contact method is greatly limited, which is shown as follows: the high-temperature extensometer has short service life and high price, and cannot measure working conditions of small gauge length and large deformation; the high-temperature strain gauge is difficult to adhere in a high-temperature environment, poor in reliability and high in price, so that the test cost is high. The non-contact deformation measurement method is generally based on the optical principle, has the advantages of full-field measurement, high precision, easy realization of automation and the like, and has good application prospect in high-temperature test. Optical non-contact methods are mainly classified into interferometric methods and non-interferometric methods. The interference method is generally based on the principle of light diffraction and has higher precision, wherein the moire interference method becomes a standard method for measuring the elastic constant of a high-temperature material, but the testing process and the data processing process of the method are complex, and the grating etching of the method cannot be applied to the measurement of an ultrathin material. A Digital Image Correlation (DIC) method in the non-interference measurement method has a wider application range and shows a good application prospect in a material elastic constant test at a high temperature.
The principle of the two-dimensional digital image correlation method is as follows: the method comprises the steps of collecting images of the front surface and the rear surface of an object before and after deformation, carrying out correlation calculation on gray scale features in the images before and after deformation of the object, searching a target sub-area with the maximum correlation with a certain sample sub-area on a reference image on the deformed image, and determining a displacement vector of the sample sub-area.
The digital image correlation method is applied to the phenomenon that the calculation correlation coefficient of partial areas is reduced when the high-temperature deformation measurement is carried out, and the phenomenon is called as the decorrelation effect. The main reasons for this are: (1) the high-temperature speckles fall off, oxidize, melt and the like at high temperature, so that the matching degree before and after deformation is reduced; (2) the sample surface is thermally radiated to cause oversaturation of the digital image brightness.
In addition, the high-temperature deformation measurement carried out by the digital image correlation method in the atmospheric environment is influenced by the disturbance of heat flow. Due to the fact that air density is unevenly distributed due to air heat convection on the optical path, the refractive indexes of air on different optical paths are inconsistent, and therefore the collected image is distorted. Due to de-correlation effects and thermal flow disturbances
In order to solve the problem of the decorrelation effect and the influence of heat flow disturbance, a large number of scholars at home and abroad carry out research on digital image correlation methods at high temperature. Experiments prove that the influence of heat radiation can be effectively eliminated by introducing the cold color light source and the narrow band-pass filter into the measuring light path. Scholars at home and abroad also put forward various manufacturing methods and processes of high-temperature speckles. The most common speckle manufacturing method at present is to mix alumina ceramic powder and an organic solvent two-component speckle material and bond the mixture on the surface of a sample by intermolecular force in a heating and curing process. The method has the advantages of wide applicability, high use temperature, small speckle layer thickness, simple curing process, no bubble and the like, but has some defects, such as larger alumina ceramic powder particles and difficulty in uniformly spraying the alumina ceramic powder particles by a common spray gun; the adhesive force of the intermolecular force is weak, and the adhesive is easy to fall off in a high-temperature test. On the corresponding digital image, the speckle quality is reduced due to the problems of speckle manufacturing, speckle shedding and the like in a partial area, so that the correlation coefficient is reduced, and the testing precision is influenced. The airflow field of the hot air has space randomness and time randomness, the space randomness causes the position of the image around a certain origin to fluctuate randomly on the horizontal and vertical coordinates under the influence of heat convection, and the time randomness shows that the sequence images in the same state show random jitter along with time. At present, the general solutions for the heat flow disturbance include gray level averaging, median filtering, mean filtering, and the like.
Disclosure of Invention
In view of the above-mentioned situation in the prior art, an object of the present invention is to provide a method for measuring a high temperature elastic constant of a thin strip, so as to solve the problem of accuracy reduction caused by decorrelation effect and heat flow disturbance when deformation is measured in a high temperature atmosphere environment by using a digital image correlation method, and the method can effectively improve the measurement accuracy of deformation in a high temperature environment.
The above object of the present invention is achieved by the following technical solutions:
a method for measuring high temperature spring constant for thin strip material, comprising the steps of:
(1) preparing a flat-plate high-temperature tensile sample, and manufacturing high-temperature speckles on the surface of the sample;
(2) respectively acquiring a reference image (hereinafter referred to as a reference image) of the flat-plate type high-temperature tensile sample in an unloaded state and images (hereinafter referred to as loaded images) of the flat-plate type high-temperature tensile sample in various loaded states;
(3) identifying a high-reliability square subregion in a reference image;
(4) comparing the reference image with the loaded image by using a digital image correlation method, and calculating the horizontal and vertical displacement of the high-reliability subarea after loading;
(5) and obtaining the elastic modulus E and the Poisson ratio mu of the sample material by weighted least square fitting based on the calculated transverse and longitudinal displacements of the high-reliability subarea after loading and the corresponding loads.
Further, in the step (1), the mass ratio of 3: 1 alumina powder and alcohol mixed to make high temperature speckles. Wherein the material is processed into a flat tensile specimen by milling, and in addition, if the specimen is too thin, a reinforcing sheet may be provided at the clamping portion.
Further, in the step (2), the acquiring of the reference image comprises continuously acquiring not less than 20 frames of images at the target temperature, and performing gray level averaging on the acquired images to obtain the reference image; and the acquisition of the loaded image comprises loading by adopting a beam displacement control method, wherein the stretching speed is not more than 0.6mm/min, and the loaded image under the corresponding load is continuously obtained.
Further, in step (4), the evaluation parameters of the reliability include a sum of squares of mean gray gradients, a temporal correlation degree, and a spatial correlation degree.
Still further, wherein identifying high reliability square sub-regions in the reference image comprises:
carrying out translation movement on the sample, training by utilizing a neural network algorithm, establishing a reliability parameter Q of the image, circularly calculating the reliability parameters Q of all the p multiplied by p subregions in the reference image under the condition that p is increased progressively, screening out the subregions with the maximum reliability value,
judging whether the screened reliability maximum sub-area contains the previously determined small-size high-reliability sub-area, judging whether the reliability parameter Q of the reliability maximum sub-area is larger than that of the contained small-size high-reliability sub-area, and if not, taking the reliability maximum sub-area as a new high-reliability sub-area; if the reliability parameter Q of the reliability maximum subregion is contained and is larger than the contained high-reliability subregion, replacing the contained high-reliability subregion by the reliability maximum subregion to be used as a new high-reliability subregion; if the reliability parameter Q of the reliability maximum sub-area is smaller than the included high reliability sub-area, the reliability maximum sub-area is not set as a new high reliability sub-area.
Further, in step (5), the weighting factor is the product of the reliability parameter of the sub-region and the area of the sub-region.
The invention considers that a large amount of full-field information redundancy exists in uniform strain fields such as stretching fields, only the displacement of the area with the highest reliability of the speckle field is needed to be calculated, the elastic constant of the material can be obtained, and the dispersity of heat flow disturbance on time and space is eliminated by utilizing the fitting between the displacements of a plurality of high-reliability sub-areas and the load. Therefore, the invention establishes a composite speckle evaluation parameter considering the average gray gradient of speckles, the spatial correlation and the time correlation, and obtains the composite speckle evaluation parameter by using a neural network algorithm in a translation test. The method of the invention can overcome the problem that the correlation coefficient of the image part area is reduced due to the decorrelation effect, effectively eliminate the random error caused by high-temperature heat flow disturbance, and finish the high-efficiency measurement of the high-temperature elastic constant by a non-contact means.
Drawings
FIG. 1 is a flow chart illustrating the steps of the method of the present invention;
FIG. 2 is a schematic diagram of a measurement system set up in implementing the method of the present invention;
FIG. 3 is a schematic representation of a flat plate sample used in the method of the present invention;
FIG. 4 is a schematic diagram of a high reliability sub-region in an image at 900 ℃ in the method of the present invention.
Detailed Description
For a clearer understanding of the objects, technical solutions and advantages of the present invention, the present invention will be described in further detail below with reference to the accompanying drawings and embodiments.
The invention provides a high-temperature elastic constant measuring method for a thin strip, which comprises the following steps (see figure 1):
s1: sample processing and high-temperature speckle manufacturing:
s11: and (5) processing the sample into a flat tensile sample by adopting a milling method. If the test specimen is too thin, the reinforcing sheet is brazed at the clamping part to ensure that the pin hole is not torn during the stretching process. If the initial surface condition is too smooth, a sanding treatment is used to increase the roughness of the surface.
S12: alumina powder and alcohol solvent are selected to produce high temperature speckle. Mixing alumina powder and an alcohol solvent according to a mass ratio of 3: 1, mixing, namely uniformly spraying the mixture on the surface of a test piece by using a pressure spray gun to form a layer of uniformly distributed speckles with high image contrast, and ensuring that the particle size of the speckles is about 3-5 pixels and the density of the speckles is about 50%; naturally drying the test piece sprayed with the speckles for 2 hours at normal temperature, then placing the test piece into a high-temperature environment box at 93 ℃ to heat for 2 hours, and taking out and naturally cooling; finally, blowing off the infirm speckles by using an ear washing ball to finish the preparation of the high-temperature speckles.
S2: constructing a high-temperature non-contact deformation measuring system:
the system consists of a high-resolution digital CCD camera, a double telecentric lens, a blue LED area array illumination light source, a narrow band-pass filter arranged in front of the lens, a signal controller, image analysis equipment, a fan, a high-temperature muffle furnace provided with an optical observation window, a testing machine and a sample. Wherein two telecentric mirror heads can weaken to a great extent from the influence of face displacement and camera lens distortion to the experimental result, and blue LED light source and narrow band-pass filter can eliminate the error that the sample heat radiation caused by a wide margin. The signal controller is connected with the tensile testing machine and the computer and is used for synchronizing the load signal of the tensile testing machine and the image signal acquired by the computer. The heating control system is used for controlling the temperature rise and the heat preservation of the high-temperature muffle furnace. The test apparatus is shown in FIG. 2.
S3: loading and image acquisition
S31: heating the sample to a set temperature in a high-temperature furnace, preserving heat until the sample is thermally stable, continuously collecting not less than 20 images in an initial load state, and collecting the images at an interval time of 5 s. The images are subjected to gray level averaging to obtain a reference image of the flat-plate type high-temperature tensile sample.
S32: loading by adopting a cross beam displacement control method, wherein the stretching speed is not more than 0.6mm/min, and coordinating the load Fn and the digital image of the surface of the sample collected under the load by using a signal controller to obtain a loading image of the flat-plate-type high-temperature stretching sample.
S4 identification of high reliability sub-regions
And selecting the reference image after the gray level average as a processing object, and identifying the high-reliability subarea. The identification of the high-reliability subarea comprises the following steps:
s41: mean Intensity Gradient (MIG), temporal correlation (ST), and spatial correlation (SS) were used as reliability evaluation intermediate parameters. The method comprises the steps of carrying out translation movement on a sample, training by utilizing a neural network algorithm, establishing reliability parameters of images, specifically comprising the steps of translating the sample up and down once respectively, and collecting 50 images in each state. And training the reliability evaluation parameter by using a neural network algorithm and taking a Mean Intensity Gradient (MIG), a time correlation degree and a space correlation degree as convolution intermediate parameters and a translation error as a target function to solve a linear composite reliability evaluation parameter Q.
Wherein the mean gray gradient of the p × p subregions
Figure BDA0002715600390000061
The time correlation represents the variance of the positions of the sub-regions in a period of time, and can be expressed as the time correlation at T time points
Figure BDA0002715600390000062
The spatial correlation degree represents the variance of the displacement error after the sub-area is translated, and after the G displacements,
Figure BDA0002715600390000063
wherein f isx(xij) And fy(xij) Is a point xijThe partial derivatives in the x and y directions are calculated by using a central difference algorithm.
S42: with increasing p, the Q of the subregions of all p pixels in the image are computed in a loop, screening out the subregion where the reliability is greatest. The initial value of p is set to 5. Judging whether the screened reliability maximum sub-area contains the previously determined small-size high-reliability sub-area, judging whether the reliability parameter Q of the reliability maximum sub-area is larger than that of the contained small-size high-reliability sub-area, and if not, taking the reliability maximum sub-area as a new high-reliability sub-area; if the reliability parameter Q of the reliability maximum subregion is larger than the contained high reliability subregion, replacing the contained high reliability subregion by the reliability maximum subregion; if the reliability parameter Q of the reliability maximum sub-area is smaller than the included high reliability sub-area, the reliability maximum sub-area is not set as a new high reliability sub-area. And after the calculation is finished, assigning p to be p +1, and circulating the calculation until p is min (H, W). Where H is the pixel length of the sample and W is the pixel width of the sample. Thereby obtaining n high reliability regions on the reference image.
S5: displacement calculation for high reliability sub-regions
Comparing the reference image with the loaded image by using a digital image correlation algorithm to obtain the corresponding position of the high-reliability sub-area in the deformed image, and further obtaining the displacement (u) of the center point of the sub-arean,i,vn,i)。
S6 calculation of elastic constant
Center point (x) of high reliability sub-regionn,i，yn,i) Displacement (u) ofn,i,vn,i) And load level FnPerforming least square operation, taking the product of the area of the subarea and the reliability parameter Q as a weighting factor, and further obtaining the elastic modulus E and the Poisson ratio mu, wherein c1And c2Is a constant term, and S is the cross-sectional area of the working section of the sample.
Figure BDA0002715600390000071
Specific examples are:
the GH605 nickel-cobalt-based wrought superalloy cold-rolled strip is adopted to process a flat tensile sample (see figure 3), the initial thickness of the flat tensile sample is 0.2mm, and reinforcing sheets with the thickness of 0.4mm are welded on two sides of a clamping section of the flat tensile sample respectively to ensure that the flat tensile sample cannot be pulled and broken in the tensile process.
Alumina powder and alcohol solvent are selected to produce high temperature speckle. Mixing alumina powder and an alcohol solvent according to a mass ratio of 3: 1, mixing, and uniformly spraying the mixture on the surface of a test piece by using a brush dripping and splashing method to form a layer of speckles which are uniformly distributed and have high image contrast. The CCD camera is used for shooting the collected image, the speckle density is about 50%, and the size of a single speckle is about 3-5 pixels. Naturally drying the test piece sprayed with the speckles for 2 hours at normal temperature, then placing the test piece into a high-temperature environment box at 93 ℃ to heat for 2 hours, and taking out and naturally cooling; finally, blowing off the infirm speckles by using an ear washing ball to finish the preparation of the high-temperature speckles.
Constructing a high-temperature non-contact deformation measuring system: the system consists of a high-resolution Baumer digital CCD camera, a Schneider double telecentric lens (working distance 368mm), a blue LED area array illumination light source (2000 lumen), a narrow band-pass filter (filtering range 450-455 nm) arranged in front of the lens, a signal controller, image analysis equipment, a pneumatic device, a high-temperature muffle furnace provided with an optical observation window, a heating control system, a tensile testing machine and a sample. The signal controller is connected with the tensile testing machine and the computer and is used for synchronizing the load signal of the tensile testing machine and the image signal acquired by the computer. The heating control system is used for controlling the temperature rise and the heat preservation of the high-temperature muffle furnace.
After applying pretightening force to the sample, heating the sample to a target temperature in a high-temperature furnace, preserving heat for 20min, continuously acquiring 20 images in the state, and acquiring the interval time for 5 s. These images are subjected to gray-scale averaging to obtain a reference image Q0.
And loading by adopting a cross beam displacement control method, wherein the stretching speed is 0.6mm/min, and coordinating the load Fn and the digital image of the surface of the sample collected under the load by using a signal controller.
With reference image Q0 as an object, a high-reliability subregion in the image is identified.
The identification of the high-reliability subarea comprises the following three steps:
in the first step, Mean Intensity Gradient (MIG), temporal correlation (ST), and spatial correlation (SS) are used as reliability evaluation parameters. The sample was translated up and down once and 50 images were acquired at each stage. And training the reliability evaluation parameter by using a neural network algorithm and taking a Mean Intensity Gradient (MIG), a time correlation degree and a space correlation degree as convolution intermediate parameters and a translation error as a target function to solve a linear composite reliability evaluation parameter expression Q.
And secondly, relying on the composite reliability parameter Q as a reliability evaluation standard. Q of all the subareas of 5 pixels multiplied by 5 pixels in the image is calculated, and the subarea with the maximum reliability value is screened out.
Mean gray scale gradient
Figure BDA0002715600390000081
Wherein f isx(xij) And fy(xij) Is a point xijThe partial derivatives in the x and y directions are calculated by using a central difference algorithm. The time correlation represents the variance of the positions of the sub-regions in a period of time, and can be expressed as the time correlation at T time points
Figure BDA0002715600390000082
The spatial correlation degree represents the variance of the displacement error after the sub-area is translated, and after the G displacements,
Figure BDA0002715600390000083
and thirdly, circularly calculating Q of all the p multiplied by p subregions in the image, and screening out the subregion with the maximum reliability. The initial value of p is set to 5. Judging whether the screened reliability maximum sub-area contains the previously determined small-size high-reliability sub-area, judging whether the reliability parameter Q of the reliability maximum sub-area is larger than that of the contained small-size high-reliability sub-area, and if not, taking the reliability maximum sub-area as a new high-reliability sub-area; if the reliability parameter Q of the reliability maximum subregion is contained and is larger than the contained high reliability subregion, replacing the contained high reliability subregion by the reliability maximum subregion; if the reliability parameter Q of the reliability maximum sub-area is contained but smaller than the contained high reliability sub-area, the reliability maximum sub-area is not regarded as a new high reliability sub-area. And after the calculation is finished, assigning p to be p +1, and circulating the calculation until p is min (H, W). Where H is the pixel length of the sample and W is the pixel width of the sample. Further, n high-reliability sub-regions are obtained on the initial image, and as shown in fig. 4, the square frame portion in the figure is the screened high-reliability sub-region.
Performing correlation calculation on the loaded images one by using a digital image correlation algorithm, selecting a reverse synthesis-Gaussian Newton (IC-GN) algorithm for a sub-pixel search algorithm, obtaining the corresponding position of the high-reliability sub-area in the deformed image, and further obtaining the displacement (u) of the center point of the sub-arean,i,vn,i)。
Center point (x) of high reliability sub-regionn,i，yn,i) Displacement (u) ofn,i,vn,i) And load level FnPerforming least square operation, taking the product of the area of the subarea and Q as a weighting factor, and further obtaining the elastic modulus E and the Poisson ratio mu, wherein c1And c2Is a constant term, and S is the cross-sectional area of the working section of the sample.
Figure BDA0002715600390000091
By the method, errors caused by a decorrelation effect and heat flow disturbance when deformation is measured in a high-temperature atmospheric environment by a digital image correlation method are eliminated, and the measurement accuracy of the elastic modulus E and the Poisson ratio mu of the GH605 nickel-cobalt-based deformation high-temperature alloy under a high-temperature condition is effectively improved.

Claims (8)

1. A method for measuring high temperature spring constant for thin strip material, comprising the steps of:
(1) preparing a flat-plate high-temperature tensile sample, and manufacturing high-temperature speckles on the surface of the sample;
(2) respectively collecting a reference image and a loading image of a flat-plate type high-temperature tensile sample;
(3) identifying a high-reliability square subregion in a reference image;
(4) comparing the reference image with the loaded image by using a digital image correlation method, and calculating the horizontal and vertical displacement of the high-reliability subarea after loading;
(5) and obtaining the elastic modulus and the Poisson ratio of the sample material by weighted least square fitting based on the calculated transverse and longitudinal displacements of the high-reliability subarea after loading and the corresponding loads.
2. The method according to claim 1, wherein in the step (1), the mass ratio of 3: 1 alumina powder and alcohol mixed to make high temperature speckles.
3. The method according to claim 1, wherein in step (2), the acquiring of the reference image comprises continuously acquiring not less than 20 frames of images at the target temperature, and performing gray-scale averaging on the acquired images to obtain the reference image; and the acquisition of the loaded image comprises loading by adopting a beam displacement control method, wherein the stretching speed is not more than 0.6mm/min, and the loaded image under the corresponding load is continuously obtained.
4. The method according to claim 1, wherein in the step (4), the evaluation parameters of the reliability include a sum of squares of mean gray gradients, a temporal correlation, and a spatial correlation.
5. The method of claim 4, wherein identifying high reliability square sub-regions in the reference image comprises subjecting the sample to translational motion, training using neural network algorithms, establishing reliability parameters for the image, iteratively calculating the reliability parameters for all p x p pixel sub-regions in the reference image with increasing p, screening out sub-regions in which reliability is greatest,
judging whether the screened reliability maximum sub-area contains the previously determined small-size high-reliability sub-area, judging whether the reliability parameter of the reliability maximum sub-area is larger than that of the contained small-size high-reliability sub-area, and if not, taking the reliability maximum sub-area as a new high-reliability sub-area; if the reliability parameter of the reliability maximum subregion is larger than the reliability parameter of the contained high-reliability subregion, replacing the contained high-reliability subregion by the reliability maximum subregion to be used as a new high-reliability subregion; if the reliability parameter of the reliability maximum sub-area is smaller than the reliability parameter of the included high-reliability sub-area, the reliability maximum sub-area is not set as a new high-reliability sub-area.
6. The method according to claim 1, wherein in step (5) the weighting factor is the product of the reliability parameter of the sub-region and the area of the sub-region.
7. The method of claim 1, wherein in step (1) the material is processed into a flat tensile specimen by milling.
8. The method of claim 7, wherein step (1) further comprises providing a reinforcing sheet at the clamping portion if the sample is too thin.
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